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Observation window
30 days
Published by official sources. Dates are shown in UTC.
Updated Oct 9, 2026, 09:41 AM UTC
Official updates
10
Published in this view
Companies publishing
1
Distinct companies in this window
Days with activity
10
Days with at least one update
Independent stories
0
0 verified US tech publishers
Cross-company mentions
0
Independent stories associated with another company
Open prediction markets
0
Separate from hiring rankings
The infographic
Varonis official publishing pulse
Publication cadence
When the updates appeared
Daily publication dates in UTC. Bars count posts; the line counts distinct companies.
| Date UTC | Updates | Companies |
|---|---|---|
| 2026-09-10 | 0 | 0 |
| 2026-09-11 | 0 | 0 |
| 2026-09-12 | 0 | 0 |
| 2026-09-13 | 0 | 0 |
| 2026-09-14 | 0 | 0 |
| 2026-09-15 | 1 | 1 |
| 2026-09-16 | 1 | 1 |
| 2026-09-17 | 0 | 0 |
| 2026-09-18 | 1 | 1 |
| 2026-09-19 | 0 | 0 |
| 2026-09-20 | 0 | 0 |
| 2026-09-21 | 1 | 1 |
| 2026-09-22 | 1 | 1 |
| 2026-09-23 | 1 | 1 |
| 2026-09-24 | 0 | 0 |
| 2026-09-25 | 1 | 1 |
| 2026-09-26 | 0 | 0 |
| 2026-09-27 | 0 | 0 |
| 2026-09-28 | 0 | 0 |
| 2026-09-29 | 0 | 0 |
| 2026-09-30 | 1 | 1 |
| 2026-10-01 | 0 | 0 |
| 2026-10-02 | 0 | 0 |
| 2026-10-03 | 0 | 0 |
| 2026-10-04 | 0 | 0 |
| 2026-10-05 | 1 | 1 |
| 2026-10-06 | 0 | 0 |
| 2026-10-07 | 1 | 1 |
| 2026-10-08 | 0 | 0 |
| 2026-10-09 | 0 | 0 |
Source breadth
Who published most
Official updates from the most active companies in this view.
| Company | Updates |
|---|---|
| Varonis | 10 |
Coverage map
Official posts and independent mentions
One story may mention several companies. Bars follow its publication date and count it once in this view.
| Date UTC | Official posts | Independent stories |
|---|---|---|
| 2026-09-10 | 0 | 0 |
| 2026-09-11 | 0 | 0 |
| 2026-09-12 | 0 | 0 |
| 2026-09-13 | 0 | 0 |
| 2026-09-14 | 0 | 0 |
| 2026-09-15 | 1 | 0 |
| 2026-09-16 | 1 | 0 |
| 2026-09-17 | 0 | 0 |
| 2026-09-18 | 1 | 0 |
| 2026-09-19 | 0 | 0 |
| 2026-09-20 | 0 | 0 |
| 2026-09-21 | 1 | 0 |
| 2026-09-22 | 1 | 0 |
| 2026-09-23 | 1 | 0 |
| 2026-09-24 | 0 | 0 |
| 2026-09-25 | 1 | 0 |
| 2026-09-26 | 0 | 0 |
| 2026-09-27 | 0 | 0 |
| 2026-09-28 | 0 | 0 |
| 2026-09-29 | 0 | 0 |
| 2026-09-30 | 1 | 0 |
| 2026-10-01 | 0 | 0 |
| 2026-10-02 | 0 | 0 |
| 2026-10-03 | 0 | 0 |
| 2026-10-04 | 0 | 0 |
| 2026-10-05 | 1 | 0 |
| 2026-10-06 | 0 | 0 |
| 2026-10-07 | 1 | 0 |
| 2026-10-08 | 0 | 0 |
| 2026-10-09 | 0 | 0 |
Listed-company context
Varonis: stock and observed roles
Stock series is indexed to 100 at its first available point. Open roles are ApplyDjinn observations; the lines have different units and do not imply causation.
| Date | Stock index | Observed roles |
|---|---|---|
| 2026-09-03 | 100.0 | 65 |
| 2026-09-04 | 99.1 | 64 |
| 2026-09-05 | 99.1 | 63 |
| 2026-09-06 | 99.1 | 63 |
| 2026-09-08 | 97.7 | |
| 2026-09-09 | 99.1 | |
| 2026-09-10 | 97.8 | 66 |
| 2026-09-11 | 97.0 | 66 |
| 2026-09-12 | 97.0 | 64 |
| 2026-09-13 | 97.0 | 62 |
| 2026-09-14 | 102.1 | 64 |
| 2026-09-15 | 102.1 | 68 |
| 2026-09-16 | 100.5 | 70 |
| 2026-09-17 | 102.8 | 62 |
| 2026-09-18 | 102.3 | 61 |
| 2026-09-19 | 102.3 | 60 |
| 2026-09-20 | 102.3 | 60 |
| 2026-09-21 | 104.2 | 60 |
| 2026-09-22 | 101.7 | 67 |
| 2026-09-23 | 104.0 | 63 |
| 2026-09-24 | 103.1 | 65 |
| 2026-09-25 | 99.4 | 62 |
| 2026-09-26 | 103.1 | 60 |
| 2026-09-27 | 99.4 | 60 |
| 2026-09-28 | 103.0 | 62 |
| 2026-09-29 | 101.3 | 63 |
| 2026-09-30 | 102.0 | 61 |
| 2026-10-01 | 102.0 | 66 |
| 2026-10-02 | 102.3 | 71 |
| 2026-10-03 | 102.0 | 70 |
| 2026-10-04 | 102.3 | 70 |
| 2026-10-05 | 103.5 | 70 |
| 2026-10-06 | 105.5 | 69 |
| 2026-10-07 | 105.3 | 71 |
| 2026-10-08 | 105.3 | 71 |
Official charts count the selected company’s own feed; independent mentions include stories from other publishers. Charts cover only ingested sources, not all market news. Stock prices and prediction markets provide context; neither proves a hiring change nor affects the hiring ranking.
Seven-day attention radar
Search and prediction signals
Google Trends shows a traffic bucket for a trending search cluster, not searches for this employer alone. Polymarket markets reflect their own question and trading activity. Neither is a hiring indicator.
No verified company matches for this signal filter yet. The monitor will keep checking.
Data source: Google Trends and Polymarket. Only exact company-name matches are shown; ambiguous names are excluded.
Chronological feed
Latest about Varonis
10 stories · page 1 of 1
varonis.com
Least Privilege for AI Agents: A Practical Guide for Security Leaders
Key takeaways Least privilege for AI agents gives each agent its own identity and only the access a specific task needs, for only as long as that task runs. Human-centric access models break down for agents, which inherit user permissions, run on shared service accounts, and act at machine speed. OWASP names excessive permissions as a root cause of excessive
varonis.com
Access Governance: Reducing Attacker Reach in the Age of AI-Accelerated Threats
Key takeaways You won't patch your way out of AI-accelerated threats. Reducing attacker reach matters more than ever. Blast radius is the metric that matters. The less an identity can access, the less damage an attacker can do. Every AI agent is an identity problem. New AI-powered access paths require the same visibility, governance, and control as human use
varonis.com
Varonis and Cohesity: Improving Cyber Resilience with Data Security
Backup datasets often contain sensitive information — from PII to intellectual property — and a wide range of business-critical data. Without visibility into their backups, security teams are left with a blind spot that increases the blast radius and impedes cyber recovery.
varonis.com
AI Security Fundamentals: The Real Industry Shift Taking Place
Key takeaways "Security for AI" isn't a distinct discipline. AI systems need the same governance, access controls, and monitoring as everything else in the stack. Adopting AI often functions as an unplanned pen test, exposing pre-existing weaknesses like excessive permissions, weak identity controls, and poor data classification. AI's real advantage for
varonis.com
Meet AvisLoader: A Windows Loader Built to Outlast a Takedown
Varonis Threat Labs recently discovered AvisLoader, a new Windows loader named after the Latin word for bird. We found it on an exposed staging server alongside a ClickFix lure, supporting tools, and its Command Center.
varonis.com
Introducing Varonis Triage Agent: An Autonomous Incident Responder to Amplify MDDR Service
Key takeaways The Varonis Triage Agent is an AI incident responder that investigates alerts like an analyst, gathering evidence and testing explanations. With the Triage Agent, MDDR analysts respond to malicious activity more quickly. The agent maintains a production recall rate above 96%, reliably surfacing real threats without burying them in false-po
varonis.com
Varonis Named a Pace Setter in the September 2026 Gartner® Emerging Market Quadrant for AI Application Security
Varonis is proud to be named Pace Setter in the Gartner® Emerging Market Quadrant for AI Application Security , recognized for our approach to securing AI applications across the entire development and deployment lifecycle.
varonis.com
Teaching a Machine to Think Like an Incident Responder
Key takeaways The Varonis Triage Agent investigates alerts like an incident responder, forming hypotheses, gathering evidence, and testing benign explanations. In one case, the agent connected three separate alerts — invisible to rule-based scoring alone — into a single incident spanning over 17,000 file downloads, giving Varonis MDDR a complete picture from
varonis.com
TrustSink: How a Rogue External MFA Provider Steals Passwords
Varonis Threat Labs identified a credential-phishing technique we call TrustSink. It turns a trusted external authentication provider into a persistent credential trap within a legitimate sign-in flow.
varonis.com
Introducing Varonis Data Lifecycle Management
Varonis Data Lifecycle Management (DLM) is a new capability that automatically finds and quarantines redundant, obsolete, and trivial (ROT) data across your entire data estate. Cut storage costs, improve AI outputs, and reduce risk, effortlessly.
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